Crowdsourcing an Internet-only Band

Hacker News

Hello HN, Longtime reader here (I'm a Ruby on Rails developer for my day job). I've dabbled in music and songwriting since high-school, but never was able to make enough connections to form a band. Wanting to put my music out there and collaborate, I decided to make a "project" out of it. I present to you, Global Music Friends: https://www.youtube.com/watch?v=yy68TpeLanQ That video explains the deal, but basically, I'm posting my music to YouTube and hoping for collaborators to contribute. We're also soliciting original songwriting and if selected, those entries will make it onto the main channel. What does HN think?

Visit product View on Hacker NewsSep 17, 2016

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4points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: plain · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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